GUI design exploration software for microwave antennas

Liu, Bo; Irvine, Alexander R.; Akinsolu, Mobayode O.; Arabi, Omer; Grout, Vic; Ali, Nazar

doi:10.1016/j.jcde.2017.04.001

Cited by 9 publications

(5 citation statements)

References 12 publications

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“…SADEA-I is implemented in MATLAB 54 and the EM simulator is CST Microwave Studio (CST-MWS). The working environment is a Red Hat Enterprise Linux Server 7.6 (Maipo 7.6 64-bit) where all EM simulations were carried out using CST-MWS distributed computing feature with Core 3.30 GHz processor and 2 Tesla M60 accelerator devices with 755.6 GB RAM.…”

Section: Example and Discussionmentioning

confidence: 99%

Machine learning-assisted lens-loaded cavity response optimization for improved direction-of-arrival estimation

Abbasi

Akinsolu

Liu

et al. 2022

Sci Rep

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This paper presents a millimeter-wave direction of arrival estimation (DoA) technique powered by dynamic aperture optimization. The frequency-diverse medium in this work is a lens-loaded oversized mmWave cavity that hosts quasi-random wave-chaotic radiation modes. The presence of the lens is shown to confine the radiation within the field of view and improve the gain of each radiation mode; hence, enhancing the accuracy of the DoA estimation. It is also shown, for the first time, that a lens loaded-cavity can be transformed into a lens-loaded dynamic aperture by introducing a mechanically controlled mode-mixing mechanism inside the cavity. This work also proposes a way of optimizing this lens-loaded dynamic aperture by exploiting the mode mixing mechanism governed by a machine learning-assisted evolutionary algorithm. The concept is verified by a series of extensive simulations of the dynamic aperture states obtained via the machine learning-assisted evolutionary optimization technique. The simulation results show a 25$$\%$$ % improvement in the conditioning for the DoA estimation using the proposed technique.

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Section: Example and Discussionmentioning

confidence: 99%

Machine learning-assisted lens-loaded cavity response optimization for improved direction-of-arrival estimation

Abbasi

Akinsolu

Liu

et al. 2022

Sci Rep

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“…Similarly, Liu et al [28] prove that analysis in the 2D radiation plot observing the H-plane is an effective means to be able to improving antenna design and exploration. The study conducted by Liu et al [29] proves that MATLAB is an efficient tool for simulating and designing antennas. This was confirmed by the group as the software contains both local and global optimization methods compatible with antenna exploration.…”

Section: Review Of Related Literaturementioning

confidence: 99%

Simulations on the effects of an optimized bowtie dipole antenna with an adaptive FIR filter

Africa

Tan

2023

Bulletin EEI

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In the evolution of technology through the years, antennas are use in varying wireless systems have been in demand. Antennas play a great role in transmitting and receiving signals. As its application is heavily used in many days to day activities, it is important to create a cost-efficient and quick way to analyze its performance, characteristics, and relationship to different variables. As many radiation pattern acquisition devices are expensive, this simulation proposes a quick, reliable, and cost-friendly way to simulate 2D patterns in the E-plane and H-plane of a bowtie dipole antenna with an adaptive finite impulse response (FIR) filter. Through this study, the software MATLAB will be utilized to successfully simulate the radiation patterns of antennas with varying lengths. With the use of MATLAB toolboxes, the researchers aim to be able to compare different antenna lengths and determine the relationship and effect of it in the obtained 2D radiation pattern. If this method is successful various antenna applications may be implemented in the future with the use of 2D radiation pattern results.

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“…The corresponding simulation times are approximately 1.5 min and 12 min, respectively, using the PC mentioned earlier. The SADEA method is implemented by the ADE tool [44] Table 3. The total design exploration time was 20 h.…”

Section: Implementation Of Msadea For the Wideband Mimo Antennamentioning

confidence: 99%

“…Lower The SADEA method is implemented by the ADE tool [44] (ade.cadescenter.com). In ADE, all the algorithm parameters are automatically set.…”

Section: Variables L2 W2 L1 W1 D1 Xvia Xportmentioning

confidence: 99%

Compact Wideband MIMO Diversity Antenna for Mobile Applications Using Multi-Layered Structure

et al. 2020

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A closely packed wideband multiple-input multiple-output (MIMO)/diversity antenna (of two ports) with a small size of less than 18.5 mm by 18.5 mm is proposed for mobile communication applications. The antenna can be orthogonally configured for corner installation or by placing it on a back-to-back structure for compact modules. To enhance the isolation and widen the bandwidth, the antenna is structured with multiple layers having differing dielectric constants. The feeding through a via significantly reduces the ground waves. A multi-fidelity surrogate model-assisted design exploration method is employed to obtain the optimized antenna geometric parameters efficiently. The antenna design was investigated using electromagnetic simulation and a physical realization of the optimal design was then created and subjected to a range of tests. The specific parameters investigated included reflection coefficients, mutual coupling between the input ports, radiation patterns, efficiency and parameters specific to MIMO behavior: envelope correlation coefficient and pattern diversity multiplexing coefficient. It was found that the antenna has an impedance bandwidth of approximately 4 GHz, mutual coupling between input ports of better than −18 dB and an envelope correlation coefficient of less than 0.002 across the operating band. This makes it a good candidate design for many mobile MIMO applications.

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GUI design exploration software for microwave antennas

Cited by 9 publications

References 12 publications

Machine learning-assisted lens-loaded cavity response optimization for improved direction-of-arrival estimation

Machine learning-assisted lens-loaded cavity response optimization for improved direction-of-arrival estimation

Simulations on the effects of an optimized bowtie dipole antenna with an adaptive FIR filter

Compact Wideband MIMO Diversity Antenna for Mobile Applications Using Multi-Layered Structure

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